AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
Desmarais, and Giuliano Antoniol
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 2years
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Prompt Coverage Adequacy, measured via attention boosting in LLMs, is associated with fault detection and uncovers over 30% more faults than traditional code coverage when guiding test generation across two datasets.
citing papers explorer
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How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
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Prompt Coverage Adequacy
Prompt Coverage Adequacy, measured via attention boosting in LLMs, is associated with fault detection and uncovers over 30% more faults than traditional code coverage when guiding test generation across two datasets.